import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import YolosImageProcessor
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Any=7   ,   lowerCamelCase_						:						Dict=3   ,   lowerCamelCase_						:						int=30   ,   lowerCamelCase_						:						str=400   ,   lowerCamelCase_						:						int=True   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						List[str]=True   ,   lowerCamelCase_						:						Optional[Any]=[0.5, 0.5, 0.5]   ,   lowerCamelCase_						:						Optional[int]=[0.5, 0.5, 0.5]   ,   lowerCamelCase_						:						Optional[Any]=True   ,   lowerCamelCase_						:						Dict=1 / 255   ,   lowerCamelCase_						:						int=True   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
    UpperCamelCase			  =      parent
    UpperCamelCase			  =      batch_size
    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      min_resolution
    UpperCamelCase			  =      max_resolution
    UpperCamelCase			  =      do_resize
    UpperCamelCase			  =      size
    UpperCamelCase			  =      do_normalize
    UpperCamelCase			  =      image_mean
    UpperCamelCase			  =      image_std
    UpperCamelCase			  =      do_rescale
    UpperCamelCase			  =      rescale_factor
    UpperCamelCase			  =      do_pad







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_rescale": self.do_rescale,
        "rescale_factor": self.rescale_factor,
        "do_pad": self.do_pad,
    }







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Any=False				):





    """simple docstring"""


    if not batched:
     UpperCamelCase			  =      image_inputs[0]
     if isinstance(lowerCamelCase_   ,   Image.Image				):
      UpperCamelCase  ,     UpperCamelCase			  =      image.size
     else:
      UpperCamelCase  ,     UpperCamelCase			  =      image.shape[1], image.shape[2]
     if w < h:
      UpperCamelCase			  =      int(self.size["""shortest_edge"""] * h / w				)
      UpperCamelCase			  =      self.size["""shortest_edge"""]
     elif w > h:
      UpperCamelCase			  =      self.size["""shortest_edge"""]
      UpperCamelCase			  =      int(self.size["""shortest_edge"""] * w / h				)
     else:
      UpperCamelCase			  =      self.size["""shortest_edge"""]
      UpperCamelCase			  =      self.size["""shortest_edge"""]

    else:
     UpperCamelCase			  =      []
     for image in image_inputs:
      UpperCamelCase  ,     UpperCamelCase			  =      self.get_expected_values([image]				)
      expected_values.append((expected_height, expected_width)				)
     UpperCamelCase			  =      max(lowerCamelCase_   ,   key=lambda lowerCamelCase_				: item[0]				)[0]
     UpperCamelCase			  =      max(lowerCamelCase_   ,   key=lambda lowerCamelCase_				: item[1]				)[1]

    return expected_height, expected_width





@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				YolosImageProcessor if is_vision_available() else None







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      YolosImageProcessingTester(self				)







   @property
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    return self.image_processor_tester.prepare_image_processor_dict()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """image_mean"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """image_std"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """do_normalize"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """do_resize"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """size"""				)				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class.from_dict(self.image_processor_dict				)
    self.assertEqual(image_processor.size   ,   {"""shortest_edge""": 18, """longest_edge""": 1333}				)
    self.assertEqual(image_processor.do_pad   ,   lowerCamelCase_				)

    UpperCamelCase			  =      self.image_processing_class.from_dict(
        self.image_processor_dict   ,   size=42   ,   max_size=84   ,   pad_and_return_pixel_mask=lowerCamelCase_				)
    self.assertEqual(image_processor.size   ,   {"""shortest_edge""": 42, """longest_edge""": 84}				)
    self.assertEqual(image_processor.do_pad   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    pass







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    # create random PIL images
    UpperCamelCase			  =      prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=lowerCamelCase_				)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_   ,   Image.Image				)

    # Test not batched input
    UpperCamelCase			  =      image_processing(image_inputs[0]   ,   return_tensors="""pt"""				).pixel_values

    UpperCamelCase  ,     UpperCamelCase			  =      self.image_processor_tester.get_expected_values(lowerCamelCase_				)

    self.assertEqual(
        encoded_images.shape   ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,   )

    # Test batched
    UpperCamelCase  ,     UpperCamelCase			  =      self.image_processor_tester.get_expected_values(lowerCamelCase_   ,   batched=lowerCamelCase_				)

    UpperCamelCase			  =      image_processing(lowerCamelCase_   ,   return_tensors="""pt"""				).pixel_values
    self.assertEqual(
        encoded_images.shape   ,   (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )   ,   )







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    # create random numpy tensors
    UpperCamelCase			  =      prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=lowerCamelCase_   ,   numpify=lowerCamelCase_				)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_   ,   np.ndarray				)

    # Test not batched input
    UpperCamelCase			  =      image_processing(image_inputs[0]   ,   return_tensors="""pt"""				).pixel_values

    UpperCamelCase  ,     UpperCamelCase			  =      self.image_processor_tester.get_expected_values(lowerCamelCase_				)

    self.assertEqual(
        encoded_images.shape   ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,   )

    # Test batched
    UpperCamelCase			  =      image_processing(lowerCamelCase_   ,   return_tensors="""pt"""				).pixel_values

    UpperCamelCase  ,     UpperCamelCase			  =      self.image_processor_tester.get_expected_values(lowerCamelCase_   ,   batched=lowerCamelCase_				)

    self.assertEqual(
        encoded_images.shape   ,   (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )   ,   )







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    # create random PyTorch tensors
    UpperCamelCase			  =      prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=lowerCamelCase_   ,   torchify=lowerCamelCase_				)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_   ,   torch.Tensor				)

    # Test not batched input
    UpperCamelCase			  =      image_processing(image_inputs[0]   ,   return_tensors="""pt"""				).pixel_values

    UpperCamelCase  ,     UpperCamelCase			  =      self.image_processor_tester.get_expected_values(lowerCamelCase_				)

    self.assertEqual(
        encoded_images.shape   ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,   )

    # Test batched
    UpperCamelCase			  =      image_processing(lowerCamelCase_   ,   return_tensors="""pt"""				).pixel_values

    UpperCamelCase  ,     UpperCamelCase			  =      self.image_processor_tester.get_expected_values(lowerCamelCase_   ,   batched=lowerCamelCase_				)

    self.assertEqual(
        encoded_images.shape   ,   (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )   ,   )







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    UpperCamelCase			  =      self.image_processing_class(do_resize=lowerCamelCase_   ,   do_normalize=lowerCamelCase_   ,   do_rescale=lowerCamelCase_				)
    # create random PyTorch tensors
    UpperCamelCase			  =      prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=lowerCamelCase_   ,   torchify=lowerCamelCase_				)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_   ,   torch.Tensor				)

    # Test whether the method "pad" and calling the image processor return the same tensors
    UpperCamelCase			  =      image_processing_a.pad(lowerCamelCase_   ,   return_tensors="""pt"""				)
    UpperCamelCase			  =      image_processing_a(lowerCamelCase_   ,   return_tensors="""pt"""				)

    self.assertTrue(
        torch.allclose(encoded_images_with_method["""pixel_values"""]   ,   encoded_images["""pixel_values"""]   ,   atol=1E-4				)				)







   @slow
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
    with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""   ,   """r"""				) as f:
     UpperCamelCase			  =      json.loads(f.read()				)

    UpperCamelCase			  =      {"""image_id""": 3_9769, """annotations""": target}

    # encode them
    UpperCamelCase			  =      YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""				)
    UpperCamelCase			  =      image_processing(images=lowerCamelCase_   ,   annotations=lowerCamelCase_   ,   return_tensors="""pt"""				)

    # verify pixel values
    UpperCamelCase			  =      torch.Size([1, 3, 800, 1066]				)
    self.assertEqual(encoding["""pixel_values"""].shape   ,   lowerCamelCase_				)

    UpperCamelCase			  =      torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
    self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,   lowerCamelCase_   ,   atol=1E-4				)				)

    # verify area
    UpperCamelCase			  =      torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,   lowerCamelCase_				)				)
    # verify boxes
    UpperCamelCase			  =      torch.Size([6, 4]				)
    self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,   lowerCamelCase_   ,   atol=1E-3				)				)
    # verify image_id
    UpperCamelCase			  =      torch.tensor([3_9769]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,   lowerCamelCase_				)				)
    # verify is_crowd
    UpperCamelCase			  =      torch.tensor([0, 0, 0, 0, 0, 0]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,   lowerCamelCase_				)				)
    # verify class_labels
    UpperCamelCase			  =      torch.tensor([75, 75, 63, 65, 17, 17]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,   lowerCamelCase_				)				)
    # verify orig_size
    UpperCamelCase			  =      torch.tensor([480, 640]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,   lowerCamelCase_				)				)
    # verify size
    UpperCamelCase			  =      torch.tensor([800, 1066]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,   lowerCamelCase_				)				)







   @slow
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
    with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""   ,   """r"""				) as f:
     UpperCamelCase			  =      json.loads(f.read()				)

    UpperCamelCase			  =      {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

    UpperCamelCase			  =      pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

    # encode them
    UpperCamelCase			  =      YolosImageProcessor(format="""coco_panoptic"""				)
    UpperCamelCase			  =      image_processing(images=lowerCamelCase_   ,   annotations=lowerCamelCase_   ,   masks_path=lowerCamelCase_   ,   return_tensors="""pt"""				)

    # verify pixel values
    UpperCamelCase			  =      torch.Size([1, 3, 800, 1066]				)
    self.assertEqual(encoding["""pixel_values"""].shape   ,   lowerCamelCase_				)

    UpperCamelCase			  =      torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
    self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,   lowerCamelCase_   ,   atol=1E-4				)				)

    # verify area
    UpperCamelCase			  =      torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,   lowerCamelCase_				)				)
    # verify boxes
    UpperCamelCase			  =      torch.Size([6, 4]				)
    self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,   lowerCamelCase_   ,   atol=1E-3				)				)
    # verify image_id
    UpperCamelCase			  =      torch.tensor([3_9769]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,   lowerCamelCase_				)				)
    # verify is_crowd
    UpperCamelCase			  =      torch.tensor([0, 0, 0, 0, 0, 0]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,   lowerCamelCase_				)				)
    # verify class_labels
    UpperCamelCase			  =      torch.tensor([17, 17, 63, 75, 75, 93]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,   lowerCamelCase_				)				)
    # verify masks
    UpperCamelCase			  =      82_2873
    self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()   ,   lowerCamelCase_				)
    # verify orig_size
    UpperCamelCase			  =      torch.tensor([480, 640]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,   lowerCamelCase_				)				)
    # verify size
    UpperCamelCase			  =      torch.tensor([800, 1066]				)
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,   lowerCamelCase_				)				)

import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE 		=      logging.getLogger()

_SCREAMING_SNAKE_CASE 		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    os.makedirs(lowerCamelCase_   ,   exist_ok=lowerCamelCase_				)
    UpperCamelCase			  =      {"""source""": """What is love ?""", """target""": """life"""}
    UpperCamelCase			  =      {"""train""": 12, """val""": 2, """test""": 2}
    for split in ["train", "test", "val"]:
     for field in ["source", "target"]:
      UpperCamelCase			  =      """\n""".join([contents[field]] * n_lines[split]				)
      with open(os.path.join(lowerCamelCase_   ,   f"""{split}.{field}"""				)   ,   """w"""				) as f:
       f.write(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str = "pytorch"				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_auto_remove_tmp_dir()
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """output"""				)
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """data"""				)
    self._create_dummy_data(data_dir=lowerCamelCase_				)

    UpperCamelCase			  =      f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

    if gpus > 0:
     testargs.append(f"""--gpus={gpus}"""				)
     if is_apex_available():
      testargs.append("""--fp16"""				)
    else:
     testargs.append("""--gpus=0"""				)
     testargs.append("""--distributed_backend=ddp_cpu"""				)
     testargs.append("""--num_processes=2"""				)

    UpperCamelCase			  =      [sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
    execute_subprocess_async(lowerCamelCase_   ,   env=self.get_env()				)

    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """metrics.json"""				)
    with open(lowerCamelCase_				) as f:
     UpperCamelCase			  =      json.load(lowerCamelCase_				)
    return result







   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=2				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)
